COVID19 Data @ Data Science Group, IBS

By Meeyoung Cha, Diego Saez-Trumper, Jing Ma, Changwook Jung, Sung Jun Park, Geng Sun

You can see more contents and graphs through our project website: COVID-19

This repository is used to share the data we collected about COVID2019. The data files are stored in the ./Data/ directory in .csv format.

In [1]:
# storing and anaysis
import numpy as np
import pandas as pd

# visualization
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns

from plotnine import *
import calmap

import plotly.express as px
import folium

Bad key "text.kerning_factor" on line 4 in
/opt/anaconda3/envs/py36/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.
You probably need to get an updated matplotlibrc file from
https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template
or from the matplotlib source distribution

File 1: LatestDailyData.csv

This document provides the latest daily updates on the status of individual countries.

In [2]:
data = pd.read_csv("./Data/LatestDailyData.csv")
In [3]:
data.head()
Out[3]:
Country/Region Province/State Confirmed Deaths Recovered Still Infected
0 Afghanistan NaN 4 0 0 4
1 Algeria NaN 19 0 0 19
2 Andorra NaN 1 0 0 1
3 Argentina NaN 12 1 0 11
4 Armenia NaN 1 0 0 1
In [4]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 254 entries, 0 to 253
Data columns (total 6 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Country/Region  254 non-null    object
 1   Province/State  154 non-null    object
 2   Confirmed       254 non-null    int64 
 3   Deaths          254 non-null    int64 
 4   Recovered       254 non-null    int64 
 5   Still Infected  254 non-null    int64 
dtypes: int64(4), object(2)
memory usage: 12.0+ KB
In [5]:
fig = px.bar(data[['Country/Region', 'Confirmed']].sort_values('Confirmed', ascending=False), 
             y="Confirmed", x="Country/Region", color='Country/Region', 
             log_y=True, template='ggplot2', title='Confirmed Cases')
fig.show()

File 2: DailyNewNumberOfWorld.csv

This document gives the total daily confirmed, deaths, and recovery in the world

In [6]:
data = pd.read_csv("./Data/DailyNewNumberOfWorld.csv")
In [7]:
data.head()
Out[7]:
Date Confirmed Deaths Recovered
0 2020-01-22 NaN NaN NaN
1 2020-01-23 98.0 1.0 2.0
2 2020-01-24 288.0 8.0 6.0
3 2020-01-25 493.0 16.0 3.0
4 2020-01-26 684.0 14.0 13.0
In [8]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 47 entries, 0 to 46
Data columns (total 4 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       47 non-null     object 
 1   Confirmed  46 non-null     float64
 2   Deaths     46 non-null     float64
 3   Recovered  46 non-null     float64
dtypes: float64(3), object(1)
memory usage: 1.6+ KB
In [9]:
fig = px.bar(data, x="Date", y="Confirmed", title='Numbers of Comfirmed All Over the world')
fig.show()
fig = px.bar(data, x="Date", y="Recovered", title='Numbers of Recovered All Over the world')
fig.show()
fig = px.bar(data, x="Date", y="Deaths", title='Numbers of Deaths All Over the world')            
fig.show()

File 3: DailyTotalNumberInEachCountry.csv

This document gives the total daily diagnoses, deaths, and recovery in each counrty respctively.

In [10]:
data = pd.read_csv("./Data/DailyTotalNumberInEachCountry.csv")
In [11]:
data.head()
Out[11]:
Unnamed: 0 Country/Region Date Confirmed Deaths Recovered
0 0 Afghanistan 2020-01-22 0 0 0
1 1 Afghanistan 2020-01-23 0 0 0
2 2 Afghanistan 2020-01-24 0 0 0
3 3 Afghanistan 2020-01-25 0 0 0
4 4 Afghanistan 2020-01-26 0 0 0
In [12]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5029 entries, 0 to 5028
Data columns (total 6 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Unnamed: 0      5029 non-null   int64 
 1   Country/Region  5029 non-null   object
 2   Date            5029 non-null   object
 3   Confirmed       5029 non-null   int64 
 4   Deaths          5029 non-null   int64 
 5   Recovered       5029 non-null   int64 
dtypes: int64(4), object(2)
memory usage: 235.9+ KB
In [13]:
fig = px.bar(data, x="Date", y="Confirmed", color='Country/Region', title='Number of Confirmed In Each Country')
fig.show()
fig = px.bar(data, x="Date", y="Recovered", color='Country/Region', title='Number of Recovered In Each Country')
fig.show()
fig = px.bar(data, x="Date", y="Deaths", color='Country/Region', title='Number of Deaths In Each Country')
fig.show()

File 4: TotalNumbersInEachCountry.csv

This document gives the total accumulative confirmed, deaths, and recovery in Each country.
It is also divided into three following files:

  • File 5: TotalConfirmedNumberInEachCountry.csv
  • File 6: TotalRecoveredNumberInEachCountry.csv
  • File 7: TotalDeathsNumberInEachCountry.csv
In [14]:
data = pd.read_csv("./Data/TotalNumbersInEachCountry.csv")
In [15]:
data.head()
Out[15]:
Unnamed: 0 Country/Region Confirmed Deaths Recovered
0 20 China 80699 3097 57320
1 47 Italy 7375 366 622
2 92 South Korea 7314 50 118
3 43 Iran 6566 194 2134
4 32 France 1126 19 12
In [16]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 107 entries, 0 to 106
Data columns (total 5 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Unnamed: 0      107 non-null    int64 
 1   Country/Region  107 non-null    object
 2   Confirmed       107 non-null    int64 
 3   Deaths          107 non-null    int64 
 4   Recovered       107 non-null    int64 
dtypes: int64(4), object(1)
memory usage: 4.3+ KB
In [17]:
fig = px.bar(data, x="Country/Region", y="Confirmed", color='Country/Region', title='Number of Confirmed In Each Country')
fig.show()
fig = px.bar(data, x="Country/Region", y="Recovered", color='Country/Region', title='Number of Recovered In Each Country')
fig.show()
fig = px.bar(data, x="Country/Region", y="Deaths", color='Country/Region', title='Number of Deaths In Each Country')
fig.show()

File 8: TotalNumbersInEachProvinceInChina.csv

This document gives the total accumulative confirmed, deaths, and recovery in each province of China. This whole data have already been included in the file 4 TotalNumbersInEachCountry.csv. Still, we list it here seperately.

In [18]:
data = pd.read_csv("./Data/TotalNumbersInEachProvinceInChina.csv")
In [19]:
data.head()
Out[19]:
Unnamed: 0 Province/State Confirmed Deaths Recovered
0 0 Hubei 67707 2986 45235
1 1 Guangdong 1352 7 1256
2 2 Henan 1272 22 1247
3 3 Zhejiang 1215 1 1161
4 4 Hunan 1018 4 968
In [20]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 31 entries, 0 to 30
Data columns (total 5 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Unnamed: 0      31 non-null     int64 
 1   Province/State  31 non-null     object
 2   Confirmed       31 non-null     int64 
 3   Deaths          31 non-null     int64 
 4   Recovered       31 non-null     int64 
dtypes: int64(4), object(1)
memory usage: 1.3+ KB
In [21]:
fig = px.bar(data, x="Province/State", y="Confirmed", color='Province/State', title='Number of Confirmed In Each Province of China')
fig.show()
fig = px.bar(data, x="Province/State", y="Recovered", color='Province/State', title='Number of Recovered In Each Province of China')
fig.show()
fig = px.bar(data, x="Province/State", y="Deaths", color='Province/State', title='Number of Deaths In Each Province of China')
fig.show()

File 9 : StandardizedTimeSeries.csv

You can se from the file's name. It provides the time series data of confirmed/recovered/deaths number in each country similar to File 3: DailyTotalNumberInEachCountry.csv. but we standardize it and list here seperately to give a close look at the different patterns of each country. So as File 10 : StandardizedTimeSeriesDataOfComfirmed.csv. We list the confirmed number seperately

In [22]:
data = pd.read_csv("./Data/StandardizedTimeSeriesData.csv")
In [23]:
data.head()
Out[23]:
Unnamed: 0 Country/Region Date Confirmed Deaths Recovered
0 0 Afghanistan 2020-01-22 NaN NaN NaN
1 1 Afghanistan 2020-01-23 0.0 0.0 0.0
2 2 Afghanistan 2020-01-24 0.0 0.0 0.0
3 3 Afghanistan 2020-01-25 0.0 0.0 0.0
4 4 Afghanistan 2020-01-26 0.0 0.0 0.0
In [24]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5029 entries, 0 to 5028
Data columns (total 6 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Unnamed: 0      5029 non-null   int64  
 1   Country/Region  5029 non-null   object 
 2   Date            5029 non-null   object 
 3   Confirmed       4922 non-null   float64
 4   Deaths          4922 non-null   float64
 5   Recovered       4922 non-null   float64
dtypes: float64(3), int64(1), object(2)
memory usage: 235.9+ KB
In [25]:
plt.style.use('ggplot')
g = sns.FacetGrid(data, col="Country/Region", hue="Country/Region", sharey=False, col_wrap=5)
g = g.map(sns.lineplot, "Date", "Confirmed")
g.set_xticklabels(rotation=90)
plt.show()
In [26]:
plt.style.use('ggplot')
g = sns.FacetGrid(data, col="Country/Region", hue="Country/Region", sharey=False, col_wrap=5)
g = g.map(sns.lineplot, "Date", "Recovered")
g.set_xticklabels(rotation=90)
plt.show()
In [27]:
plt.style.use('ggplot')
g = sns.FacetGrid(data, col="Country/Region", hue="Country/Region", sharey=False, col_wrap=5)
g = g.map(sns.lineplot, "Date", "Deaths")
g.set_xticklabels(rotation=90)
plt.show()

File 10 : StandardizedTimeSeriesDataOfComfirmed.csv

Included in File 9 StandardizedTimeSeries.csv. Listed seperately to enhance it.

In [28]:
data = pd.read_csv("./Data/StandardizedTimeSeriesDataOfComfirmed.csv")
In [29]:
data.head()
Out[29]:
Unnamed: 0 Date Country/Region Confirmed
0 0 2020-01-22 Afghanistan 0
1 1 2020-01-22 Algeria 0
2 2 2020-01-22 Andorra 0
3 3 2020-01-22 Argentina 0
4 4 2020-01-22 Armenia 0
In [30]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5029 entries, 0 to 5028
Data columns (total 4 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Unnamed: 0      5029 non-null   int64 
 1   Date            5029 non-null   object
 2   Country/Region  5029 non-null   object
 3   Confirmed       5029 non-null   int64 
dtypes: int64(2), object(2)
memory usage: 157.3+ KB
In [31]:
plt.style.use('ggplot')
g = sns.FacetGrid(data, col="Country/Region", hue="Country/Region", sharey=False, col_wrap=5)
g = g.map(sns.lineplot, "Date", "Confirmed")
g.set_xticklabels(rotation=90)
plt.show()